I think this looks very good, and I love seeing that it has the capability to automatically select the recommendation algorithm with the best performance.
I think there are two major areas where this can be improved. A quick read through the docs doesn't show much info about the algorithms themselves, and from what I can see, it doesn't look like you can customize the hyperparameters of the algorithms (correct me if I'm wrong). You have to just wait for the automated hyperparameter search to finish. I think this is mostly very good but the ability to freeze them would be even better to save compute time in subsequent retrainings. The other thing I notice is that there's no system to detect when the offline model is too out of date and trigger retraining. It would be nice to have a way of automating the retraining based on performance evaluations rather than on a clock schedule.
I think there are two major areas where this can be improved. A quick read through the docs doesn't show much info about the algorithms themselves, and from what I can see, it doesn't look like you can customize the hyperparameters of the algorithms (correct me if I'm wrong). You have to just wait for the automated hyperparameter search to finish. I think this is mostly very good but the ability to freeze them would be even better to save compute time in subsequent retrainings. The other thing I notice is that there's no system to detect when the offline model is too out of date and trigger retraining. It would be nice to have a way of automating the retraining based on performance evaluations rather than on a clock schedule.